P83 Remote pulmonary function testing – computer gaming in the respiratory world. (12th November 2015)

• Record Type: Journal Article
• Authors: Sharp, C; Soleimani, V; Hannuna, S; Camplani, M; Damen, D; Viner, J; Mirmehdi, M; Dodd, J
• Is Part Of: Thorax. Volume 70(2015)Supplement 3
• Page Start: A117
• Page End: A118
• Publication Date: 2015-11-12
• Subjects: Chest -- Diseases -- Periodicals; Thorax; Chest -- Diseases; Periodicals; Periodicals; 617.54
• DOI: 10.1136/thoraxjnl-2015-207770.220
• Languages: English
• ISSNs: 0040-6376

Abstract

Abstract : Introduction: Lung function testing by spirometry has remained unchanged for over 50 years, despite limitations including patient technique, discomfort, cost and training. Non-invasive, remote lung volume measurement is an alternative approach. This has been explored in the past using structured light, accelerometers and impedance pneumography, but these have often been costly and of limited validity. We use a novel approach to remote assessment (~2 metres) using a 3D time-of-flight depth camera – similar to those found in many home gaming consoles. This pilot developmental data was generated from patients in a clinical setting. Methods: Patients were recruited from a general respiratory physiology laboratory. Spirometry was performed according to ATS/ERS standards using an unmodified pneumotachograph (nSpire Health, Longmont, CO, USA). A Kinect V2 time-of-flight depth sensor (Microsoft, Redmond, WA, USA) was used to reconstruct 3D models of the subject's thorax to estimate volume-time and flow-time curves for both Forced and Slow Vital Capacity and their associated measurements (Figure 1, technical details in 1 ). These results were correlated with simultaneous recordings from the pneumotachograph, and error values calculated to assess the accuracy of the technique. Results: Data were available from 53 patients, with 40 having usable data. Mean age 62.8 yrs (SD 16.2), BMI of 26.8 (SD 5.5). 41.5% male. 54.7% of patients had obstructive lung diseases, and 28.4% fibrotic lung disease. Mean FVC was 91.3% predicted (SD 26.4%), Mean FEV1 83.1% (SD 28.9%). The model estimates were highly correlated with spirometric values for FVC (λ = 0.999), FEV1 (λ = 0.947), VC (λ = 0.999), IC (λ = 0.997) and TV (λ = 0.962). Univariate analysis demonstrated no patient characteristics predictive of discrepancy from spirometric values for FVC or VC. Conclusions: We describe a pilot data from the initial development of a new technique for non-invasively assessing lung volume and pulmonary function measurements. It correlates to within 30 ml for FVC and 10 ml for VC. This has a wide range of potential applications, including screening, home monitoring of respiratory disease, assessment of lung function in those unable to complete pneumotachygraphy and gating controls for radiological imaging techniques. Reference: 1 Soleimani V, Mirmehdi M, Damen D, et al . Remote pulmonary function testing using a depth sensor. Biocas 2015